Research article

MWaste: An app that uses deep learning to manage household waste

  • Received: 07 April 2023 Revised: 04 June 2023 Accepted: 19 June 2023 Published: 12 July 2023
  • Computer vision methods are effective in classifying garbage into recycling categories for waste processing but existing methods are costly, imprecise and unclear. To tackle this issue we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.

    Citation: Suman Kunwar. MWaste: An app that uses deep learning to manage household waste[J]. Clean Technologies and Recycling, 2023, 3(3): 119-133. doi: 10.3934/ctr.2023008

    Related Papers:

  • Computer vision methods are effective in classifying garbage into recycling categories for waste processing but existing methods are costly, imprecise and unclear. To tackle this issue we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92% on the test set. This app can help combat climate change by enabling efficient waste processing and reducing the generation of greenhouse gases caused by incorrect waste disposal.



    加载中


    [1] Kaza S, Yao LC, Bhada-Tata P, et al. (2018) What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050, Washington DC: World Bank. https://doi.org/10.1596/978-1-4648-1329-0
    [2] Widener E (1991) Waste minimization. Proceedings Frontiers in Education Twenty-First Annual Conference. Engineering Education in a New World Order, IEEE, 101–104. https://doi.org/10.1109/FIE.1991.187446
    [3] Ankaram S (2019) Reuse and recycling: An approach for sustainable waste management, Re-Use and Recycling of Materials: Solid Waste Management and Water Treatment, Abingdon: Routledge, 3–13. https://doi.org/10.1201/9781003339304-2
    [4] Muawad SAT, Omara AAM (2019) Waste to energy as an alternative energy source and waste management solution. 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 1–6. https://doi.org/10.1109/ICCCEEE46830.2019.9071080 doi: 10.1109/ICCCEEE46830.2019.9071080
    [5] Lai K, Li L, Mutti S, et al. (2014) Evaluation of waste reduction and diversion as alternatives to landfill disposal. 2014 Systems and Information Engineering Design Symposium (SIEDS), 183–187. https://doi.org/10.1109/SIEDS.2014.6829877 doi: 10.1109/SIEDS.2014.6829877
    [6] Mridha K, Shaw RN, Ghosh A (2021) Intelligent based waste management awareness developed by transfer learning. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), 1–5. https://doi.org/10.1109/GUCON50781.2021.9573586 doi: 10.1109/GUCON50781.2021.9573586
    [7] Ferronato N, Torretta V (2019) Waste mismanagement in developing countries: a review of global issues. Int J Environ Res Public Health 16: 1060. https://doi.org/10.3390/ijerph16061060 doi: 10.3390/ijerph16061060
    [8] Iqbal S, Naz T, Naseem M (2021) Challenges and opportunities linked with waste management under global perspective: a mini review. J Qual Assur Agric Sci 1: 9–13. https://doi.org/10.52862/jqaas.2021.1.1.2 doi: 10.52862/jqaas.2021.1.1.2
    [9] Fadhullah W, Imran NIN, Ismail SNS, et al. (2022) Household solid waste management practices and perceptions among residents in the East Coast of Malaysia. BMC Public Health 22: 1–20. https://doi.org/10.1186/s12889-021-12274-7. doi: 10.1186/s12889-021-12274-7
    [10] Lin K, Zhao Y, Gao X, et al. (2022) Applying a deep residual network coupling with transfer learning for recyclable waste sorting. Environ Sci Pollut Res 29: 91081–91095. https://doi.org/10.1007/s11356-022-22167-w doi: 10.1007/s11356-022-22167-w
    [11] Liu W, Ouyang H, Liu Q, et al. (2022) Image recognition for garbage classification based on transfer learning and model fusion. Math Probl Eng 2022: 4793555. https://doi.org/10.1155/2022/4793555 doi: 10.1155/2022/4793555
    [12] Rashida J, Hamzah R, Samah KAFA, et al. (2022) Implementation of faster region-based convolutional neural network for waste type classification. 2022 International Conference on Computer and Drone Applications (IConDA), 125–130. https://doi.org/10.1109/ICONDA56696.2022.10000369 doi: 10.1109/ICONDA56696.2022.10000369
    [13] Nafiz MS, Das SS, Morol MK, et al. (2023) Convowaste: An automatic waste segregation machine using deep learning. 2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 181–186. https://doi.org/10.1109/ICREST57604.2023.10070078 doi: 10.1109/ICREST57604.2023.10070078
    [14] Cheema SM, Hannan A, Pires IM (2022) Smart waste management and classification systems using cutting edge approach. Sustainability 14: 10226. https://doi.org/10.3390/su141610226 doi: 10.3390/su141610226
    [15] Shreya M, Katyal J, Ramesh R, et al. (2022) Technical solutions for waste classification and management: A mini-review. Waste Manage Res 41: 801. https://doi.org/10.1177/0734242X221135262 doi: 10.1177/0734242X221135262
    [16] Narayan Y (2021) DeepWaste: Applying deep learning to waste classification for a sustainable planet. arXiv Preprint
    [17] Feng J, Tang X (2020) Office garbage intelligent classification based on inception-v3 transfer learning model. J Phys Conf Ser 1487: 012008. https://doi.org/10.1088/1742-6596/1487/1/012008 doi: 10.1088/1742-6596/1487/1/012008
    [18] Yong L, Ma L, Sun D, et al. (2023) Application of MobileNetV2 to waste classification. PLoS One 18: e0282336. https://doi.org/10.1371/journal.pone.0282336 doi: 10.1371/journal.pone.0282336
    [19] Lee SW (2021) Novel classification method of plastic wastes with optimal hyperparameter tuning of Inception_ResnetV2. 2021 4th International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, IEEE, 274–279. https://doi.org/10.1109/ICOIACT53268.2021.9563917
    [20] Girsang AS, Yunanda R, Syahputra ME, et al. (2022) Convolutional neural network using res-net for organic and anorganic waste classification. 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM), 1–6. https://doi.org/10.1109/ICOSNIKOM56551.2022.10034869 doi: 10.1109/ICOSNIKOM56551.2022.10034869
    [21] Nurahmadan IF, Arjuna RM, Prasetyo HD, et al. (2021) A mobile based waste classification using mobilenets-v1 architecture. 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 279–284. https://doi.org/10.1109/ICIMCIS53775.2021.9699161 doi: 10.1109/ICIMCIS53775.2021.9699161
    [22] Rismiyati, Endah SN, Khadijah, et al. (2020) Xception architecture transfer learning for garbage classification. 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 1–4. https://doi.org/10.1109/ICICoS51170.2020.9299017 doi: 10.1109/ICICoS51170.2020.9299017
    [23] Yang M, Thung G, Trashnet. GitHub, 2017. Available from: https://github.com/garythung/trashnet.
    [24] Ho Y, Wookey S (2020) The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access 8: 4806–4813. https://doi.org/10.1109/ACCESS.2019.2962617 doi: 10.1109/ACCESS.2019.2962617
    [25] Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. arXiv Preprint https://doi.org/10.1016/j.neucom.2018.07.079
    [26] Zhang B, Zhao Q, Feng W, et al. (2018) Alphamex: A smarter global pooling method for convolutional neural networks. Neurocomputing 321: 36–48. https://www.sciencedirect.com/science/article/pii/S0925231218310610
    [27] Salehinejad H, Valaee S (2019) Ising-dropout: A regularization method for training and compression of deep neural networks. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), 3602–3606. https://doi.org/10.1109/ICASSP.2019.8682914 doi: 10.1109/ICASSP.2019.8682914
    [28] Zhou Y, Wang X, Zhang M, et al. (2019) Mpce: a maximum probability based cross entropy loss function for neural network classification. IEEE Access 7: 146331–146341. https://doi.org/10.1109/ACCESS.2019.2946264 doi: 10.1109/ACCESS.2019.2946264
    [29] Li J, Gao M, D'Agostino R (2019) Evaluating classification accuracy for modern learning approaches. Stat Med 38: 2477–2503. https://doi.org/10.1002/sim.8103 doi: 10.1002/sim.8103
    [30] Uzen H, Turkoglu M, Hanbay D (2021) Surface defect detection using deep u-net network architectures. 2021 29th Signal Processing and Communications Applications Conference (SIU), IEEE, 1–4. https://doi.org/10.1109/SIU53274.2021.9477790
    [31] Wardhani NWS, Rochayani MY, Iriany A, et al. (2019) Cross-validation metrics for evaluating classification performance on imbalanced data. 2019 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Tangerang, Indonesia, IEEE, 14–18. https://doi.org/10.1109/IC3INA48034.2019.8949568
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3017) PDF downloads(317) Cited by(1)

Article outline

Figures and Tables

Figures(6)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog